Pacemakers providing CRT to a patient are known. One such device is disclosed, for example, in published European Application Publication No. EP 1108446 A1, filed Dec. 12, 2000, published Jun. 20, 2001, entitled “Implantable Active Device of Type Multisite Having Means for Resynchronization of Ventricles,” and its counterpart U.S. Pat. No. 6,556,866 (assigned to Sorin CRM, previously known as ELA Medical), which is incorporated herein by reference and describes a device applying, between the respective instants of stimulation to the left and the right ventricles, a delay known as a variable interventricular delay (VVD). The VVD is adjusted so as to resynchronize the contraction of both left and right ventricles with fine optimization and improve the hemodynamic status of the patient.
A simultaneous stimulation of both left and right ventricles is not always optimal because it does not necessarily lead to a synchronous contraction of the ventricles. The delays of conduction in the left and right ventricular myocardium are not the same and may depend on multiple factors such as the location of the ventricular leads and the type of the ventricular leads (e.g., an over-the-wire lead implanted into the coronary sinus or an epicardial lead). It is therefore desirable to establish a delay between the two stimuli (i.e., VVD) and to adjust the delay VVD to resynchronize the contractions of the ventricles ensuring a fine optimization of hemodynamic parameters. Depending on the patient's clinical status, the VVD may be set to zero, positive (the left ventricle is stimulated after the right ventricle) or negative (the right ventricle is stimulated after the left ventricle).
A typical CRT pacemaker device runs a classic “dual chamber” operating mode in which the device monitors the ventricular activity after an atrial event that is either spontaneous (P wave detection of an atrial depolarization) or stimulated (application of an A pulse of atrial pacing). After detecting an atrial event, the device starts to count a delay period called “atrio-ventricular delay” (AVD) such that if no spontaneous ventricular activity (R wave detection of a ventricular depolarization) is detected after this AVD, then the device triggers a stimulation of the ventricle (application of a V pulse of ventricular pacing).
Clinical studies have demonstrated often dramatic improvements for patients with congestive heart failure (“CHF”) whose condition was not improved by conventional CHF therapies by precisely adjusting the parameters of the CRT therapy according to the patient's clinical condition and to the nature of specific myocardial contraction disorders such as dilated heart chambers, low ejection fraction, and excessive lengthening of the duration for the QRS complex (whether this disorder is spontaneous or induced by a traditional stimulation).
It is also necessary to reassess these parameter settings to optionally re-adjust them if necessary: indeed, the benefits provided by the CRT therapy may eventually lead to modifying the original configuration and the parameter settings.
The standard technique for adjusting CRT stimulation parameters starts with the estimation of the characteristic delays of the systole by echocardiography, especially the timing of opening of the aortic valve. However, this procedure should be implemented in hospitals and by qualified personnel. This procedure is long and costly, thus cannot be applied as often as it would be useful or necessary without interfering with the patient's daily life, despite the beneficial effects on the CRT therapy.
Another inherent difficulty with the echocardiographic assessment is that it requires testing several successive pacing configurations, and determining an optimal AVD for each pacing configuration. Therefore, the number of combinations to be tested is important, and involves a complicated and time consuming procedure that is difficult to manage excluding it from a routine operation.
Therefore, there is a need for a technique for evaluating in a simple, rapid, automated and precise procedure the impact of different CRT stimulation parameters, including the AVD and VVD delays, so as to optimize the patient's hemodynamic status.
One automated method of optimization is described in the article by J M Dupuis, et al.: Programming Optimal Atrioventricular Delay in Dual Chamber Pacing Using Peak Endocardial Acceleration: Comparison with a Standard Echocardiographic Procedure, PACE 2003; 26: [Pt II], 210-213. This technique involves, while scanning the AVD in a given stimulation setup to trace a characteristic, generating a value of the peak of endocardial acceleration (“PEA”) according to the AVD. The considered optimal value of the AVD is the inflection point of the characteristics, i.e., the point corresponding to a maximum duration of ventricular filling without truncating the A wave (i.e., a minimum delay between the closing of the mitral valve and the beginning of the QRS complex).
Even if the corresponding algorithm gives satisfactory results, it takes up to several minutes before an optimal pair {AVD, VVD} is selected because multiple scans of AVD are required for various values of VVD that are separately tested.
Another drawback of this optimization technique is that the search for each delay period (AVD or VVD) is independent of the other: for a given VVD, a scan of the AVD gives a locally optimal AVD, but (as is explained in greater detail below with particular reference to FIG. 5) the convergence to a local optimum does not necessarily lead to the global optimum. In other words, the optimal pair {AVD, VVD} does not necessarily correspond to an optimal AVD value, at constant VVD, or to an optimal VVD value, at constant AVD.
A special technique of optimization, which is faster, thus implementable in real time, is described in WO 2006/090397 A2 and WO 2006/126185 A2. The algorithm described in these documents use a spike neural network to identify the maximum of a hemodynamic function (stroke volume). A spike network, however, requires a dedicated processor, thus involving the design of a specific, more complex device demanding higher power consumption. A software implementation of this algorithm is possible, but in such a case, it requires computing resources that are unattainable in ultra-low power consumption microcontrollers adequate for use in implantable medical devices.
WO 2008/010220 describes yet another technique, in which a spike neural processor is combined with a reinforced learning algorithm (Q-learning), which learns and associates the cardiac conditions to the optimal delays. Using this Q-learning can offer improved immunity to noise and increase the speed of convergence of the control algorithm. However, in order to achieve this performance, additional hardware resources are required including a microprocessor in addition to the spike neural processor, which incurs extra cost, higher power consumption, and increased spatial requirements for an implant device.